Information processing systems comprising virtual data centers (VDCs) and other types of cloud infrastructure are coming into increasingly widespread use. For example, commercially available virtualization software may be used to build a wide variety of different types of cloud infrastructure, including private and public cloud computing and storage systems, distributed across hundreds of interconnected physical computers and storage devices. As the complexity of such cloud infrastructure increases, the need for accurate and efficient management of system resources has also grown.
This management of system resources is often implemented using system management tools such as quality of service (QoS) managers, data migration managers, resource managers, load balancers, workflow managers and event handlers.
An issue that arises when using conventional system management tools is that such tools often do not have a sufficiently broad view of all of the various situations that may impact the operating performance of their corresponding managed systems, particularly in the case of managed systems that comprise cloud infrastructure. For example, such management tools often base their policy decisions solely on local knowledge, or other types of limited knowledge that does not adequately reflect high-level situations that may have a significant impact on operating performance of the managed systems. Also, policy setting in conventional system management tools typically requires significant human intervention, for example, from an administrator or other employee of a corresponding enterprise.